We use an ImageNet pre-trained deep learning model named DenseNet [1]} to extract visual features from video. The video is split into T clips. As in [2]}, we choose T = 10, so each clip is one second long without overlapping. For each clip, we extract the output of the DenseNet last convolutional layer for 16 RGB video frames with a global average pooling over the 16 frames to generate one \(7\times 7 \times 1920\)  dimensional feature map.
